Optimal Foraging

Welcome Back!

This week we are going to discuss optimal foraging and what it is as well as how to determine if an individual is an optimal forager.

An optimal forager is one that forages in a manner that will maximize the individuals net energy gain. Now what is this ‘net energy gain’? Well net energy gain is the difference between the energy gained by an individual and the energy spent by that individual. As you can imagine, survival in the wild depends on if an individual is an optimal forager. An individual must make the decision of when to leave a patch of food source in search of another. The individual can decide to leave when food is becoming scarce in the patch or deplete all the food resources in this patch before leaving in search of another. The choice of when to leave is a factor in determining if an individual is an optimal forager.

While this is important to animals in the wild, humans (for the most part) are no longer dependent on hunting and gathering. Thanks grocery stores! Of course there are still places in the world where people are semi or solely dependent on hunting and gathering which is why it is important for ecologists to study. While I was in Africa I discovered that one of the purposes of the township dogs was to assist in hunting.  The men in the village would go out and track down antelope with their dogs. Yes, these men took down the same prey that LIONS take down. I can say from experience that most of these antelope species are strong and massive! Obviously humans cannot out run these fast creatures so that`s what they used their dogs for. These men had to decide where to hunt and when to move to the next hunting spot. If they do this in a way to maximize net energy gain then they are optimal foragers!

You maybe asking yourself, how do we determine if an individual is an optimal forager. Well you have the mind of an ecologist you ask those kind of questions! I did an experiment that tested predictions of an optimal foraging model. The experiment was set up outside because, of course, that is where foraging takes place after all. There were buckets filled with rice that were equidistantly spaced. These buckets represented different patches of food sources. Each rice bucket contained a different amount of beans to represent the differing amount of food source in each patch. This set-up mimics the natural setting in the wild where food source is often distributed in high density and low density amounts in different patches.  If you can remember from my previous blog, we learned about different dispersal patterns of plants. We learned about uniform, random, and clumped dispersal patterns. The Dallisgrass followed the clumped dispersal pattern. If the buckets in this lab represented Dallisgrass then it would follow the uniform dispersal pattern! See how everything connects in ecology?

Imagine you are a little kid on Easter morning going Easter egg hunting with 20 other kids. You want to get the most amount of eggs without expending too much energy. This means that you must decide when to leave a ‘patch’ when the eggs become more scarce. This is exactly how the rice bucket and bean experiment went! The timer started when I started moving towards my first bucket. I foraged through the rice and picked out my first bean as the time of this ‘capture’ was recorded. I plopped this bean in the cup in my hand and swirled it three times. This was done to represent the consumption time of a real forager. After the third swirl I went to forage in the bucket for my second bean, time of capture was recorded, I swirled three times, and then foraged again for my third bean. Forage, find, record, swirl. Lather, rinse, repeat. This was done until I made the decision as a forager when to move to the next patch. This decision was based off when my food source was becoming more scarce and taking longer to find.  The time was recorded from when I first began to search for my first patch until I left the third patch. Each captured bean was recorded as well when I arrived at each patch and left each patch. This was repeated for three separate buckets and then they were graphed to determine if I was an optimal forager.

Before we look at these graphs I will explain what the Marginal Value Theorem states. A forager should capture more prey in patches with higher density and they should spend more time in these high density patches compared to low density patches. Also, a forager should catch more prey per unit time in dense prey patches than in low density patches. Finally, a forager should leave the patch when the intake rate has declined below the average rate. This is called the giving-up time or G.U.T. Here is one last explanation of the graphs before we analyze them. An imaginary line goes through the origin which represents the ratio of time and cumulative energy. The steeper the slope of this line means the more the net energy gain will be.

Let`s take a look at these graphs and determine if I am an optimal forager!

This scatter-plot has a polynomial trend line and depicts patch density on the x-axis and the number of beans found in each patch n the y-axis. Simply stated, the patch density is how many beans were contained in each of the three buckets, or patches, that I foraged from. The number of beans found is the amount of beans that I foraged from each bucket before leaving in search of another. As you can see I follow the prediction from the Marginal Value Theorem that states a forager should capture more prey in patches with higher density. As you can see the patches I chose had a density of 20, 40, and 80 beans. I foraged 3/20 beans, 16/40 beans, and 28/80 beans. So I did great for this prediction of the Marginal Value Theorem! Let`s see how I did with the others!

This graph has patch density as the x-axis and time spent in each patch as the y-axis. This graph shows that I spent more time in the higher density patch than the lower density patch. This follows the prediction for the Marginal Value Theorem that states a forager should spend more time in these high density patches compared to low density patches. My graph shows that I followed this prediction. Yay, two for two so far! Let`s check out this next graph of capture rate as a function of patch density.

This scatter-plot shows patch density as the x-axis and capture rate as the y-axis. Lets look back at the prediction of the Marginal Value Theorem to see if I follow it. The prediction states that a forager should catch more prey per unit time in dense prey patches than in low density patches.As you can see I left the patch with 80 beans in it too quickly and should have stayed longer to optimize my foraging. I guess I`m not as great of a forager as I thought I was! Let`s check out the next graph to see if I follow the prediction from the Marginal Value Theorem.

Finally we have the last graph showing if I follow the Prediction of the Marginal Value Theorem. If you recall from earlier, the last prediction was: a forager should leave the patch when the intake rate has declined below the average rate. This is called the giving-up time or G.U.T. Unfortunately this graph shows that I did a poor job choosing when to leave the patch. I left the low density patch after 5 seconds of finding my last prey. In the mid-density patch my GUT was 6 seconds after finding my last prey. My GUT in the high density patch was 1 second. The last graph showed I left the high density patch too soon and this also proves that. The GUT versus patch density scatter plot relates directly to the Marginal Value Theorem. The data suggests that this theorem may not apply to the behavior of humans in this experiment. Human foraging and animal foraging just cannot be compared because we have different cognitive function and instincts. This may be why this graph did not follow the Marginal Value Theorem. Lets check out our final graph!

This scatter plot shows cumulative number of prey captured as a function of cumulative time elapsed. To be an optimal forager I would need my lines to be somewhat curved (remember that imaginary line we talked about!). Looking at this graph you can see that I did fairly well overall because my lines all have a decent curve to them. In patch 3 I left too soon which you can tell due to the lack of a great curve. I did very good in patch 2 since it has a very good curve. I also left at an optimal time in patch 2.

While this experiment was conducted on humans, animals forage daily. I`m sure you noticed squirrels running around with nuts in their mouth or possibly a bird carrying worms to its young. Animals with a set territory may not forage in this way. A study was conducted on bears in the Sierra Nevada and was analyzed in an article titled, “Fast-Food Nation is taking its toll on Bears, too”  by Henry Fountain. It discusses how bears near urban areas have a different foraging pattern than bears who stuck to nature. These bears were termed ‘city bears’ and ‘country bears’. The country bears worked harder to get their food and foraged for longer while the city bears got more food faster. The bear changed its behavior in order to optimize its foraging. Also, the city bears sleeping patterns were altered to avoid humans due to this change in foraging pattern. Wildlife-human contact can be dangerous so bear-proof trash containers may be an answer to this risk. Foraging strategies are important to study in ecology because it gives us some insight into the animals around us. Learning more about foraging patterns can also help us learn more about related things such as migration patterns, food preference, etc.! Again, everything is ecology is connected. Foraging is an important topic of ecological study and one that has been discussed in the Journal of Mammalogy in the article The Ecology of Fear: Optimal Foraging, Game Theory, and Tropic Interaction. This article states that “viewing predator-prey systems as foraging games of stealth and fear
offers refreshing avenues for research and management”. Therefore, studying foraging is important and can offer insight into different ecological studies!

Until next time!

References:

Joel S. Brown, John W. Laundré, Mahesh Gurung; The Ecology of Fear: Optimal Foraging, Game Theory, and Trophic Interactions, Journal of Mammalogy, Volume 80, Issue 2, 20 May 1999, Pages 385–399